See what it takes to make AI accurate and trustworthy
Gartner® estimates that by 2027, 75% of new analytics content will be contextualized for intelligence applications through generative AI. So why is trust still such a hurdle for enterprise AI adoption?
Hallucinations, biased outputs, and gaps in training data can still be major blockers, especially for high-value use cases where the stakes are high.
Join industry experts from Snowflake and ThoughtSpot for a practical look at how to build enterprise AI that's accurate, explainable, and ready for real-world use.
You’ll learn:
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Why hallucinations happen and how to prevent them
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Why human-in-the-loop design is critical to building trust
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Where your peers are seeing real wins—and what to avoid
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How AI literacy fuels adoption and a modern data culture
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How diverse, well-governed data improves AI output
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The role of RAG architecture in improving accuracy
This is more than a crash course on preventing AI bias—it’s how you go from just testing AI to scaling it with confidence.
